Conference: IEEE International Conference on Data Mining (Singapore) - ICDM2018 (accepted)
Author: Qitian Wu, Chaoqi Yang, Xiaofeng Gao, Peng He, Guihai Chen
Title: EPAB: Early Pattern Aware Bayesian Model for Social Content Popularity Prediction
cluster the early pattern
- Influence(h1): how many people have been influenced by this tweet.
- Attractiveness(h2): how many people tend to click and repost this tweet.
- Potentiality(h3): how many people will be exposed to this tweet.
- optimize the loss function between ground truth and predicted value
- get alpha, beta, gamma for each pattern
- and get h1, h2, h3 for each cascade.
(3). two-layer Bayesian network to model observable feature X, hidden variable H, and final state Y.
- refer to the paper for detailed deduction
- bayesian rule
(4). Solve the loss function, get the model.
- to solve this loss function, we can compute the loss of theta1, theta3, and theta2 seperately
- use stochastic gradient descent and hill-climbing
Edited on Sep. 10th, 2018